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Quantification in-the-wild: data-sets and baselines
Oscar Beijbom, Judy Hoffman, Evan Yao +4
Quantification is the task of estimating the class-distribution of a data-set. While typically considered as a parameter estimation problem with strict assumptions on the data-set…
Constrained Structured Regression with Convolutional Neural Networks
Deepak Pathak, Philipp Krähenbühl, Stella X. Yu +1
Convolutional Neural Networks (CNNs) have recently emerged as the dominant model in computer vision. If provided with enough training data, they predict almost any visual quantity.…
Fine-grained pose prediction, normalization, and recognition
Ning Zhang, Evan Shelhamer, Yang Gao +1
Pose variation and subtle differences in appearance are key challenges to fine-grained classification. While deep networks have markedly improved general recognition, many approach…
Mapping Images to Sentiment Adjective Noun Pairs with Factorized Neural Nets
Takuya Narihira, Damian Borth, Stella X. Yu +2
We consider the visual sentiment task of mapping an image to an adjective noun pair (ANP) such as "cute baby". To capture the two-factor structure of our ANP semantics as well as t…
Adapting Deep Visuomotor Representations with Weak Pairwise Constraints
Eric Tzeng, Coline Devin, Judy Hoffman +5
Real-world robotics problems often occur in domains that differ significantly from the robot's prior training environment. For many robotic control tasks, real world experience is…
Auxiliary Image Regularization for Deep CNNs with Noisy Labels
Samaneh Azadi, Jiashi Feng, Stefanie Jegelka +1
Precisely-labeled data sets with sufficient amount of samples are very important for training deep convolutional neural networks (CNNs). However, many of the available real-world d…